FUZZY CLASSIFIER TRAINING WITH ONLY MAIN COMPETITORS

Authors

  • S. D. Shtovba Vinnytsia National Technical University
  • A. V. Halushchak Vinnytsia National Technical University

Keywords:

classification, fuzzy knowledge base, training, training criteria, main competitors

Abstract

The tie "input—output" is described by linguistic «if—then» rules where antecedents contain fuzzy terms "low", "medium", "high" in the fuzzy classifiers. To enhance the correctness it is necessary to train fuzzy classifier on experimental data. There have been proposed new criteria for fuzzy classifier training that take into account the difference of fuzzy output only to the main competitors. When the classification is correct the main competitor of the decision is the class with the second largest degree of membership. In cases of misclassification erroneous decision is the main competitor to the correct class.

Computer experiments with the tuning up of a fuzzy classifier for UCI-problem of recognition of Italian wines showed a significant advantage of the new training criteria. New criteria of training can be used not only for tuning fuzzy classifiers but for some other models, such as neural networks.

Author Biographies

S. D. Shtovba, Vinnytsia National Technical University

Dr. Sc. (Eng.), Professor, Professor of the Chair of Computer Control Systems

A. V. Halushchak, Vinnytsia National Technical University

Assistant of the Chair of Computer Control Systems

References

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Published

2016-03-16

How to Cite

[1]
S. D. Shtovba and A. V. Halushchak, “FUZZY CLASSIFIER TRAINING WITH ONLY MAIN COMPETITORS”, Вісник ВПІ, no. 1, pp. 124–132, Mar. 2016.

Issue

No. 1 (2016)

Section

Information technologies and computer sciences

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